Senior Machine Learning Engineer (Remote)

At RXNT, we see ourselves as more than a healthtech company—we're the digital backbone of the U.S. healthcare system. Every day, our platforms empower healthcare professionals to deliver better patient care, streamline medication and lab ordering, simplify billing and insurance processes, and strengthen connections across the entire healthcare ecosystem. For over 25 years, we've been innovating at the intersection of healthcare and technology. That history has taught us a profound truth: transforming healthcare is both a privilege and a responsibility. It requires us to uphold the highest standards, think with rigor, and move with urgency when patients' lives are on the line. As we expand our AI-driven product offerings, we're tackling some of the most exciting and complex challenges in machine learning and healthcare. Saving the healthcare providers hours of after-hours work and burnout, providing the means for a patient-focused care, and improving the care accuracy and reliability are but a few of key values we are offering to the health system today using AI. We're looking for curious, self-motivated Senior Machine Learning Engineers with strong Data Science background who thrive in fast-moving environments, embrace ownership, and never stop learning. You'll join a team that combines the energy of a startup with the stability and trust of a company that has spent decades shaping healthcare technology. Led by seasoned entrepreneurs and technologists, you'll have the opportunity to push boundaries, solve meaningful problems, and help define the future of AI in healthcare. We're looking for an experienced MLE: Proven Track Record: You have successfully delivered and maintained mission-critical models deployed at scale, ideally in high-stakes or highly regulated environments. Deep Data Science Expertise: You know that the best models are built on deeply understood data. You are highly comfortable spending the bulk of your time in the trenches—analyzing data, finding patterns, and rigorously defining the problem. Strong ML & GenAI Foundations: You possess a deep understanding of core machine learning disciplines, coupled with hands-on experience in advanced GenAI techniques, LLM fine-tuning, and robust prompt engineering. Full-Lifecycle Ownership: You don't just hand off a Jupyter notebook. You write robust, production-grade code and are comfortable with owning everything from initial data exploration to training, custom evaluations, and post-live monitoring. Agentic AI Focus: You are excited by and experienced in orchestrating complex ML workflows, building self-improving AI agents, and deploying them reliably into production. Responsibilities Own the Problem Space: Translate ambiguous product concepts into concrete ML strategies. You will start deep in the data, conducting rigorous EDA, uncovering patterns, and identifying features—long before you write production code. Architect for Healthcare: Fine-tune multi-modal generative models and orchestrate sophisticated AI agents (e.g., LangGraph) with a deep respect for the domain. You will prioritize precision, test relentlessly for edge cases, and build fallback heuristics. Establish Rigorous Evals: Define strict statistical and business metrics from day one. Build robust evaluation frameworks to ensure models meet the high-stakes reliability required in healthtech. Ship & Monitor: Write production-grade Python and collaborate with engineering to deploy scalable solutions. You own the post-deployment reality: work with engineering and ops to build monitoring for system health, track data drift, catch model degradation, and more. Communicate & Execute: Operate with deep focus and autonomy. Proactively align with stakeholders and deliver crisp, concise, results-driven updates to technical leadership. Qualifications Experience: 5+ years of professional experience in machine learning engineering and/or data science. Education: MS or PhD in Computer Science, Math, AI, or a related discipline (or a BSc with equivalent, proven experience). Deep ML Mastery: Strong statistical foundation with expert knowledge of model architectures, data analysis, and rigorous evaluation methods. Proficient in major frameworks (e.g., PyTorch, TensorFlow, JAX) and advanced fine-tuning techniques. Production & MLOps: While not a key part of your responsibilities, you have practical familiarity with containerization, orchestration, cloud services (AWS, GCP, or Azure), and CI/CD pipelines to keep production ML systems healthy, monitored, and up